Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "136" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460017 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.850718 | -0.771551 | 9.318631 | -0.435691 | 7.374281 | 0.820344 | 0.905584 | -0.336525 | 0.0373 | 0.5420 | 0.3981 | nan | nan |
| 2460016 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.917508 | 0.267408 | 10.394394 | -0.513387 | 5.604830 | 0.619433 | 1.610939 | 0.360835 | 0.0383 | 0.5527 | 0.4109 | nan | nan |
| 2460015 | digital_ok | 100.00% | 99.95% | 99.89% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.0814 | 0.2649 | 0.2222 | nan | nan |
| 2460014 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.076939 | -0.417948 | 8.065013 | -0.474203 | 8.847531 | 0.208870 | 1.085805 | -0.354413 | 0.0364 | 0.5403 | 0.4015 | nan | nan |
| 2460013 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.268607 | 1.953299 | 10.765248 | -0.518025 | 5.973378 | 0.489010 | 2.373540 | 0.914785 | 0.0387 | 0.5602 | 0.4156 | nan | nan |
| 2460012 | digital_ok | 100.00% | 99.95% | 99.95% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 1.0000 | 1.0000 | 0.0055 | nan | nan |
| 2460011 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.893576 | 0.405338 | 14.014249 | -1.276142 | 13.514940 | 0.324917 | 2.267179 | 0.512638 | 0.0423 | 0.5690 | 0.4037 | nan | nan |
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.854421 | 9.049778 | 12.571721 | -0.282139 | 7.347669 | 0.042834 | 1.601396 | 1.323896 | 0.0417 | 0.5760 | 0.3933 | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 13.142418 | 0.562724 | 13.734156 | -0.879095 | 6.660834 | 0.815606 | 4.725490 | 1.155816 | 0.0451 | 0.6373 | 0.4333 | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.782288 | 0.079443 | 10.733317 | -0.436067 | 5.942460 | 0.176834 | 2.319944 | -0.131541 | 0.0416 | 0.5933 | 0.4087 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.91% | 99.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.2409 | 0.2051 | 0.1660 | nan | nan |
| 2459998 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.300419 | -0.079840 | 9.183746 | -0.449278 | 8.030806 | 0.687268 | 1.565751 | -0.015878 | 0.0380 | 0.5931 | 0.4426 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.074434 | 0.035284 | 9.735434 | -0.405499 | 7.737407 | 0.161493 | 2.823899 | -0.093686 | 0.0426 | 0.6068 | 0.4588 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.030860 | 7.940554 | 12.254443 | -0.146664 | 7.326006 | -0.082860 | 0.971075 | 0.967069 | 0.0400 | 0.5999 | 0.4342 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.261316 | 3.893450 | 11.340187 | -0.638362 | 8.081006 | 1.213568 | 0.944236 | 0.369781 | 0.0463 | 0.5974 | 0.4372 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.898905 | -0.391281 | 9.774185 | -0.508261 | 7.846616 | 0.796701 | 2.077243 | 0.594847 | 0.0396 | 0.5978 | 0.4402 | nan | nan |
| 2459993 | digital_ok | 100.00% | 99.10% | 0.00% | 0.00% | - | - | 11.004522 | -0.227454 | 9.063503 | -0.647673 | 10.223448 | 0.457025 | 1.231145 | -0.455365 | 0.0348 | 0.5999 | 0.4218 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.762303 | -0.585037 | 9.612858 | -0.603817 | 9.212933 | 0.702106 | 0.809156 | -0.495819 | 0.0384 | 0.6033 | 0.4568 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.555378 | -0.482390 | 9.408672 | -0.617230 | 9.112542 | 0.422416 | 0.759233 | -0.715835 | 0.0421 | 0.6025 | 0.4559 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.338250 | -0.600428 | 8.371643 | -0.314291 | 8.050698 | 0.411253 | 0.475130 | -0.724747 | 0.0375 | 0.6012 | 0.4580 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.221633 | -0.505182 | 9.704072 | -0.789224 | 10.836299 | -0.108442 | 0.577571 | -0.630004 | 0.0377 | 0.6006 | 0.4481 | nan | nan |
| 2459987 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.145320 | -0.337375 | 9.421753 | -0.590823 | 6.418315 | 0.375380 | 1.579876 | -0.560062 | 0.0425 | 0.6076 | 0.4576 | nan | nan |
| 2459986 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.524738 | 1.172137 | 10.319562 | -0.701592 | 9.433541 | 0.722387 | 5.820237 | -0.656770 | 0.0399 | 0.6264 | 0.4438 | nan | nan |
| 2459985 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.533786 | 4.626973 | 9.567834 | -0.473939 | 7.279150 | 0.811017 | 1.950550 | 0.020446 | 0.0396 | 0.5942 | 0.4472 | nan | nan |
| 2459984 | digital_ok | 100.00% | 98.65% | 0.00% | 0.00% | - | - | 9.979405 | 8.050072 | 9.933745 | -0.423625 | 9.420960 | -0.427381 | 2.664149 | -0.367228 | 0.0451 | 0.6069 | 0.4419 | nan | nan |
| 2459983 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.767980 | 0.433497 | 9.488424 | -0.698342 | 9.310478 | -0.147872 | 3.571554 | -0.640262 | 0.0424 | 0.6452 | 0.4664 | nan | nan |
| 2459982 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.259825 | 0.127520 | 8.046483 | -0.341891 | 4.545913 | 0.320445 | 2.479536 | -0.041509 | 0.0410 | 0.6749 | 0.4558 | nan | nan |
| 2459981 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.154665 | -0.314822 | 10.098912 | -0.914097 | 10.471258 | 0.287931 | 0.872801 | -0.705512 | 0.0426 | 0.6086 | 0.4593 | nan | nan |
| 2459980 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 8.928972 | -0.154681 | 9.078900 | -0.771280 | 9.064243 | 0.126977 | 5.256028 | -0.639101 | 0.0427 | 0.6477 | 0.4546 | nan | nan |
| 2459979 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.344687 | -0.460309 | 8.394099 | -0.724959 | 8.981478 | 0.489372 | 0.980294 | -0.580309 | 0.0404 | 0.6064 | 0.4610 | nan | nan |
| 2459978 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.435945 | -0.092818 | 9.122595 | -0.806526 | 9.385157 | 0.132127 | 0.698442 | -0.977439 | 0.0366 | 0.6031 | 0.4568 | nan | nan |
| 2459977 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.772654 | 0.144642 | 8.976728 | -0.722799 | 9.229164 | 0.013456 | 0.901199 | 0.245669 | 0.0438 | 0.5647 | 0.4217 | nan | nan |
| 2459976 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.651653 | 0.782188 | 9.439413 | -0.774238 | 9.476611 | 1.257060 | 1.203827 | 1.317528 | 0.0381 | 0.6064 | 0.4487 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 9.318631 | -0.771551 | 8.850718 | -0.435691 | 9.318631 | 0.820344 | 7.374281 | -0.336525 | 0.905584 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 10.394394 | 0.267408 | 9.917508 | -0.513387 | 10.394394 | 0.619433 | 5.604830 | 0.360835 | 1.610939 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 10.076939 | 10.076939 | -0.417948 | 8.065013 | -0.474203 | 8.847531 | 0.208870 | 1.085805 | -0.354413 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 10.765248 | 10.268607 | 1.953299 | 10.765248 | -0.518025 | 5.973378 | 0.489010 | 2.373540 | 0.914785 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 14.014249 | 10.893576 | 0.405338 | 14.014249 | -1.276142 | 13.514940 | 0.324917 | 2.267179 | 0.512638 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 12.571721 | 10.854421 | 9.049778 | 12.571721 | -0.282139 | 7.347669 | 0.042834 | 1.601396 | 1.323896 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 13.734156 | 0.562724 | 13.142418 | -0.879095 | 13.734156 | 0.815606 | 6.660834 | 1.155816 | 4.725490 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 10.733317 | 9.782288 | 0.079443 | 10.733317 | -0.436067 | 5.942460 | 0.176834 | 2.319944 | -0.131541 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 9.183746 | 8.300419 | -0.079840 | 9.183746 | -0.449278 | 8.030806 | 0.687268 | 1.565751 | -0.015878 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 9.735434 | 9.074434 | 0.035284 | 9.735434 | -0.405499 | 7.737407 | 0.161493 | 2.823899 | -0.093686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 12.254443 | 10.030860 | 7.940554 | 12.254443 | -0.146664 | 7.326006 | -0.082860 | 0.971075 | 0.967069 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 11.340187 | 10.261316 | 3.893450 | 11.340187 | -0.638362 | 8.081006 | 1.213568 | 0.944236 | 0.369781 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.898905 | 9.898905 | -0.391281 | 9.774185 | -0.508261 | 7.846616 | 0.796701 | 2.077243 | 0.594847 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 11.004522 | 11.004522 | -0.227454 | 9.063503 | -0.647673 | 10.223448 | 0.457025 | 1.231145 | -0.455365 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 11.762303 | 11.762303 | -0.585037 | 9.612858 | -0.603817 | 9.212933 | 0.702106 | 0.809156 | -0.495819 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.555378 | -0.482390 | 9.555378 | -0.617230 | 9.408672 | 0.422416 | 9.112542 | -0.715835 | 0.759233 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.338250 | -0.600428 | 9.338250 | -0.314291 | 8.371643 | 0.411253 | 8.050698 | -0.724747 | 0.475130 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 11.221633 | -0.505182 | 11.221633 | -0.789224 | 9.704072 | -0.108442 | 10.836299 | -0.630004 | 0.577571 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 9.421753 | 9.145320 | -0.337375 | 9.421753 | -0.590823 | 6.418315 | 0.375380 | 1.579876 | -0.560062 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 11.524738 | 1.172137 | 11.524738 | -0.701592 | 10.319562 | 0.722387 | 9.433541 | -0.656770 | 5.820237 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 10.533786 | 4.626973 | 10.533786 | -0.473939 | 9.567834 | 0.811017 | 7.279150 | 0.020446 | 1.950550 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.979405 | 9.979405 | 8.050072 | 9.933745 | -0.423625 | 9.420960 | -0.427381 | 2.664149 | -0.367228 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.767980 | 9.767980 | 0.433497 | 9.488424 | -0.698342 | 9.310478 | -0.147872 | 3.571554 | -0.640262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 8.259825 | 8.259825 | 0.127520 | 8.046483 | -0.341891 | 4.545913 | 0.320445 | 2.479536 | -0.041509 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Temporal Variability | 10.471258 | -0.314822 | 9.154665 | -0.914097 | 10.098912 | 0.287931 | 10.471258 | -0.705512 | 0.872801 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Power | 9.078900 | -0.154681 | 8.928972 | -0.771280 | 9.078900 | 0.126977 | 9.064243 | -0.639101 | 5.256028 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.344687 | 9.344687 | -0.460309 | 8.394099 | -0.724959 | 8.981478 | 0.489372 | 0.980294 | -0.580309 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.435945 | -0.092818 | 9.435945 | -0.806526 | 9.122595 | 0.132127 | 9.385157 | -0.977439 | 0.698442 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.772654 | 9.772654 | 0.144642 | 8.976728 | -0.722799 | 9.229164 | 0.013456 | 0.901199 | 0.245669 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 136 | N12 | digital_ok | ee Shape | 9.651653 | 0.782188 | 9.651653 | -0.774238 | 9.439413 | 1.257060 | 9.476611 | 1.317528 | 1.203827 |